Prediction of Oxygen Content Using Weighted PCA and Improved LSTM Network in MSWI Process

Abstract

The accurate and real-time measurement of oxygen content in flue gas is the cornerstone of high incineration efficiency and economic benefits for municipal solid waste incineration (MSWI) plants. However, conventional hardware oxygen analyzers are difficult to obtain the oxygen content in flue gas timely and precisely. In this article, a weighted principal component analysis (WPCA) algorithm combined with improved long short-term memory (ILSTM) network is proposed for oxygen content prediction. First, to reduce the model complexity, a WPCA is developed to calculate mutual information correlation coefficients between principal components and the quality variable. Second, the LSTM network is exploited to establish a prediction model, and its hyperparameters are obtained with the particle swarm optimization (PSO) algorithm to improve the generalization ability of the prediction model. Finally, the effectiveness of the proposed prediction method is validated by a benchmark simulation and the real industrial data. And the comparison results with other methodologies demonstrate the considerable prediction performance of the proposed WPCA-ILSTM model.